Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024
Overestimation of contralateral hilar lymph node metastasis in non-metastatic non-small cell lung cancer and its predictive model: HAM.
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Hou Zan, Lin Xiaoping, Dong Baiqiang, Lin Zaishan, Zhang Yuan, Liu Xu, Wu Chenfei, Xu Qingqing, Wang Ying, Chen Keying, Li Qiwen, Chen Ming
Аннотация
BACKGROUND AND PURPOSE: Metastasis of non-metastatic non-small cell lung cancer (NMNSCLC) to contralateral hilar lymph nodes (CHLN) eliminates the opportunity for radical therapy. This study aims to analyze whether CHLN metastasis in NMNSCLC is commonly overestimated in clinical practice and to establish a predictive model for enhanced precision.
METHODS AND MATERIALS: We conducted a retrospective analysis of 834 pathologically confirmed NMNSCLC patients. Monitoring of treatment responses and regular ≥ 1 year CT follow-up was used to determine the nature of CHLN. Lasso regression was used to select predictive factors, and a multivariate binary logistic regression model (HAM) was constructed. Internal validation was performed using ten-fold cross-validation.
RESULTS: The CHLN metastasis rate was 4.4% among the NMNSCLC patients. The positive predictive value (PPV) and sensitivity for PET-CT diagnosis were 36.8% and 67.5%, while for CT they are 44.8% and 70.2%, respectively. The five optimal predictive factors (emphysema or bullae, central-type lung cancer, short diameter of CHLN, calcification and SUVmax) were used to develop the HAM model. The Area under curve (AUC) values for PET-CT, CT, and HAM model were 0.81, 0.83, and 0.96, respectively. The F1 scores for PET-CT and CT were 0.48 and 0.55, respectively, while the maximum F1 score of our model was 0.73, with corresponding PPV and sensitivity of 66.7%, and 81.1%, respectively.
CONCLUSIONS: CHLN metastasis is rare in NMNSCLC patients. PET-CT diagnosis significantly overestimates CHLN metastasis and the HAM model improves clinical decision-making in this study. Prospective studies are needed to confirm these conclusions.
METHODS AND MATERIALS: We conducted a retrospective analysis of 834 pathologically confirmed NMNSCLC patients. Monitoring of treatment responses and regular ≥ 1 year CT follow-up was used to determine the nature of CHLN. Lasso regression was used to select predictive factors, and a multivariate binary logistic regression model (HAM) was constructed. Internal validation was performed using ten-fold cross-validation.
RESULTS: The CHLN metastasis rate was 4.4% among the NMNSCLC patients. The positive predictive value (PPV) and sensitivity for PET-CT diagnosis were 36.8% and 67.5%, while for CT they are 44.8% and 70.2%, respectively. The five optimal predictive factors (emphysema or bullae, central-type lung cancer, short diameter of CHLN, calcification and SUVmax) were used to develop the HAM model. The Area under curve (AUC) values for PET-CT, CT, and HAM model were 0.81, 0.83, and 0.96, respectively. The F1 scores for PET-CT and CT were 0.48 and 0.55, respectively, while the maximum F1 score of our model was 0.73, with corresponding PPV and sensitivity of 66.7%, and 81.1%, respectively.
CONCLUSIONS: CHLN metastasis is rare in NMNSCLC patients. PET-CT diagnosis significantly overestimates CHLN metastasis and the HAM model improves clinical decision-making in this study. Prospective studies are needed to confirm these conclusions.